Automated production optimization technique for smart well completions using real-time nodal analysis

ABSTRACT

Systems and methods include a method for multi-segmented oil production. A multi-segmented well production model representing production at a multi-segmented oil production facility is calibrated. The model models production based on well rates and flowing bottom-hole pressure data at various choke settings for multiple flow conditions for each segment of the multi-segmented well. Real-time updates to the well rates and the flowing bottom-hole pressure data are received. Changes to triggers identifying thresholds for identifying production improvements are received. The model is re-calibrated based on the changes to the triggers and the real-time updates. An optimization algorithm is executed to determine new optimal inflow control valve (ICV) settings. Using the re-calibrated multi-segmented well production model, a determination is made whether the new optimal ICV settings improve production. If so, the optimal ICV settings are provided to a control panel for the multi-segmented oil production facility.

BACKGROUND

The present disclosure applies to optimization of well production.

Many conventional wells (for example, oil wells) are instrumented withdownhole sensors and valves that enable real-time monitoring and controlof multi-segmented (for example, multi-lateral) completions. However,engineers seldom utilize the downhole valves to adjust the productionfrom a particular zone in a timely manner. Even if such adjustments dooccur, the process can be cumbersome and is typically based on trial anderror.

SUMMARY

The present disclosure describes techniques that include real-time nodalanalysis to automate production optimization for smart well completions.For example, the techniques can use modeling techniques that combinesegment production data and estimations of downhole parameters toprovide optimum inflow control valve (ICV) settings to improve theperformance of multi-segmented (for example, multi-lateral) wells.Optimum settings can be defined, for example, as settings that maximizethe production of a multi-segmented well over a period of time. In someimplementations, automated model optimization can be used to performoptimizations in real-time. For example, the term real-time cancorrespond to events that occur within a specified period of time, suchas within one minute, within one second, or within milliseconds.

In some implementations, a computer-implemented method includes thefollowing. A multi-segmented well production model representingproduction at a multi-segmented oil production facility is calibrated.The multi-segmented well production model models production based onwell rates and flowing bottom-hole pressure data at various chokesettings for multiple flow conditions for each segment of themulti-segmented well. Real-time updates to the well rates and theflowing bottom-hole pressure data are received from the multi-segmentedoil production facility. Changes to triggers identifying thresholds foridentifying production improvements are received based on user inputs.The multi-segmented well production model is re-calibrated based on thechanges to the triggers and the real-time updates to the well rates andthe flowing bottom-hole pressure data. Using the re-calibrated nodalmodel, an optimization algorithm is executed to determine new optimalinflow control valve (ICV) settings. Using the re-calibratedmulti-segmented well production model, a determination is made whetherthe new optimal ICV settings improve production at the multi-segmentedoil production facility. In response to determining that the new optimalICV settings improve production, the optimal ICV settings are providedto a control panel for the multi-segmented oil production facility.

The previously described implementation is implementable using acomputer-implemented method; a non-transitory, computer-readable mediumstoring computer-readable instructions to perform thecomputer-implemented method; and a computer-implemented system includinga computer memory interoperably coupled with a hardware processorconfigured to perform the computer-implemented method/the instructionsstored on the non-transitory, computer-readable medium.

The subject matter described in this specification can be implemented inparticular implementations, so as to realize one or more of thefollowing advantages. First, immediate system adjustment can be madeautomatically in response to changes in production conditions. This canprovide improvements over manual practices, for example, in whichchanges are implemented after a manual review, which can results inunfavorable well settings. By comparison, automatic system adjustmentscan increase cumulative oil well production. Second, conventionaloptimization procedures of multi-segmented ICVs can be cumbersome andmay only optimize initial production conditions. This can create issuesin the later life of the well when, for example, the initial ICVsettings are no longer optimal. Third, the use of the techniques of thepresent disclosure can facilitate better management, by productionengineers, of multi-segmented well production, for example, fulfillingboth short- and long-term objectives for optimizing well production andimproving recovery. Moreover, a reduction of operating expenditures(OPEX) can be realized. Fourth, the value added from smart wellcompletion (SWC) can provide operational savings over conventionalpractices.

The details of one or more implementations of the subject matter of thisspecification are set forth in the Detailed Description, theaccompanying drawings, and the claims. Other features, aspects, andadvantages of the subject matter will become apparent from the DetailedDescription, the claims, and the accompanying drawings.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of an example of a multi-zonal smart wellcompletion, according to some implementations of the present disclosure.

FIG. 2 is a block diagram showing an example of data flow paths in asmart well completion system using real-time nodal analysis, accordingto some implementations of the present disclosure.

FIG. 3 is a screenshot of an example of an inflow control valve (ICV)optimization graphical user interface (GUI), according to someimplementations of the present disclosure.

FIG. 4 is a flow diagram showing an example of an overall optimizationworkflow, according to some implementations of the present disclosure.

FIG. 5 is a set diagram showing an example of an intersection ofdifferent well types, according to some implementations of the presentdisclosure.

FIG. 6 is a flowchart of an example method for determining new optimalICV settings for improving production at a multi-segmented oilproduction facility and providing the optimal ICV settings to a controlpanel for the multi-segmented oil production facility, according to someimplementations of the present disclosure.

FIG. 7 is a block diagram illustrating an example computer system usedto provide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure, according to some implementationsof the present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

The following detailed description describes techniques for usingreal-time nodal analysis to automate production optimization in smartwell completions. Various modifications, alterations, and permutationsof the disclosed implementations can be made and will be readilyapparent to those of ordinary skill in the art, and the generalprinciples defined may be applied to other implementations andapplications, without departing from scope of the disclosure. In someinstances, details unnecessary to obtain an understanding of thedescribed subject matter may be omitted so as to not obscure one or moredescribed implementations with unnecessary detail and inasmuch as suchdetails are within the skill of one of ordinary skill in the art. Thepresent disclosure is not intended to be limited to the described orillustrated implementations, but to be accorded the widest scopeconsistent with the described principles and features.

In general, smart (or intelligent) well completions (SWCs) are used tomaximize multi-segmented (for example, multi-lateral) well productivity,restrict unwanted water and gas production, and improve sweepefficiency. To achieve the optimum economic values of smart completions,for example, surface and subsurface chock valves settings need to befrequently optimized using various techniques. Applying the rightoptimization technique can ensure, for example, a successful andefficient optimization. An increase in global utilization of the SWCscan increase the reliance on field optimization requirements of downholeinflow control valves (ICVs). Automated optimization techniques, forexample, can eliminate the need for weeks of field-testing by providinga methodical approach to make ICV changes. Optimum (or optimal) settingscan be defined, for example, as settings that maximize the production ofa multi-segmented well over a period of time.

The present disclosure describes a production optimization approachusing real-time data and nodal model for multi-segmented wells. In someimplementations, optimization algorithms can be used to assist reservoirand production engineers in determining optimum production scenarios forcomplex wells, such as multi-segmented wells. For example, based on datafrom the current production conditions of a well, an algorithm canrecommend changes to downhole valve settings that allow for optimizedproduction.

Multi-segmented wells can be equipped with surface and subsurfacedownhole valves to provide real-time pressure and temperature dataalong. At the same time, a surface flow meter can measure multi-phaseflow. Moreover, wells can contain ICV providing various choke settingsto restrict flow based on the orifice size of each valve position.Optimization algorithms can use field data collected during a regularoptimization approach and well control parameter (for example, ICVsettings) regression, for example, using a commercial steady-statemodel. The optimization algorithms can estimate flowing parameters ofindividual segments, determine the optimum pressure drop across eachdownhole valve, and estimate productivity of each segment duringcommingled production at various choke valves settings. In someimplementations, an optimum flow scenario can be determined using agenetic optimization algorithm that iteratively manipulates ICV valvesettings using the calibrated model to provide maximum oil productionand minimum water production.

The techniques of the present disclosure, including the optimizationalgorithms, were successfully field-tested in real time and validated,using generated models used to predict well performance at variousconditions. The field testing started by collecting well rates andflowing bottom-hole pressure data at various chokes settings for twoflow conditions; commingled and individual segment testing. The acquireddata was used to calibrate the model. After model calibration, anoptimization algorithm was used to generate different productionscenarios and optimize the performance of each segment.

FIG. 1 is a diagram of an example of a multi-zonal smart well completion100, according to some implementations of the present disclosure. Eachzone 102 (for example, a zone 102 a) can be isolated with packers 104and 106 and equipped with downhole pressure gauges 108 and a valve 110.As shown in FIG. 1 , zones 102 are 100 feet (ft) long and have differentpermeabilities of 1000, 300, 600, and 100 millidarcies (md), forexample. Each zone 102 can further contain a segment well. The packers104 and 106 can include, for example, external swell packers, which arerun with a screen, and internal swell packers, which run withcompletion. To achieve optimal economic values of the multi-zonal smartwell completion 100, the surface and subsurface choke valves settingscan be frequently optimized using various techniques. Applying the rightoptimization techniques can ensure a successful and efficientoptimization.

The multi-zonal smart well completion 100 includes a safety valve 112, aproduction packer 114, and an ICV 116 that, as shown in FIG. 1 , islocated in a gas layer 118. The multi-zonal smart well completion 100also includes an oil layer 120 (containing the zones 102) and a waterlayer 122. Other arrangements of layers are possible.

In some implementations, optimization algorithms (for example, formulti-segmented wells) can use artificial intelligence techniques suchas a genetic algorithm (GA). Genetic algorithms include stochastic andheuristic search techniques based on the theory of natural selection andevolution to achieve a “survival of the fittest” solution. The use ofsuch algorithms can lead to suggesting ICV settings in multi-segmentedwells, which can then be used as input for a nodal model. In return, theresult of the nodal model can be fed back into the algorithm to evaluateeach solution presented by the algorithm.

In some implementations, the model may recommend only one set ofsettings for the ICVs in a multi-segmented well. Optimization algorithms(for example, genetic algorithms) can be implemented as stochasticmethods to assure repeatability. The algorithms can be run multipletimes, with the number of iterations used being sufficient for the sizeof the problem. For example, the number of iterations used can be basedon learned patterns of correlating the number of iterations with anumber of variables (for example, ICVs). The optimized solution canreflect the current state of the calibrated model and can be implementedimmediately to begin realizing production gains. After a productioninterval has occurred in which well/reservoir conditions are expected(or likely) to change, the calibration and optimization of the model canbe repeated to reflect these changes. In some implementations, automatedprocesses can be used to apply the recommendations to the field, forexample, as part of a closed-loop automated optimization. Models can bewell-dependent, with each well having its own model modeling segmentsand well completion.

FIG. 2 is a block diagram showing an example of data flow paths in asmart well completion system 200 using real-time nodal analysis,according to some implementations of the present disclosure. The smartwell completion system 200 can be a multi-segmented well optimizingsystem, for example. The data flow paths can include a data floworiginating from a well 202 and flowing through a productivity index(PI) system 204, and ultimately to a modeling and optimization station(for example, an engineer's desk 206). The data flow can continue to acontrol system 208 and back again to the well 202. The SWC system 200can be used, for example, to automatically apply optimum ICV settingsfor a smart well completion. In some implementations, a SWC procedureusing the SWC system 200 can include four main steps (or work flows) toachieve continuous real-time closed-loop optimization.

In a first main step of the SWC procedure, flow rate and pressure datacan be received or retrieved from the well and collected into a centrallocation. The flow rate and pressure data can be collected from wellsthat are equipped with surface and subsurface downhole valves andintegrated in an integrated operations setup, for example, as shown inFIGS. 1 and 2 .

In a second main step of the SWC procedure, a nodal analysis model canbe constructed for initial use and subsequent update. In someimplementations, initial model construction can follow the techniquesdescribed in U.S. patent application Ser. No. 16/414,535, filed on May16, 2019, titled “Automated Production Optimization Technique for SmartWell Completions Using Real-Time Nodal Analysis,” hereby incorporated inits entirety and summarized here. For example, during regular fieldoptimization procedures, a multi-segmented well can be produced onesegment at a time. Production and surface and downhole pressures can berecorded for these tests. The data can be used to calibrate a commercialsteady-state model. The model can then be used to estimate flowingparameters of individual segments, determine the optimum pressure dropacross each downhole valve, and estimate the productivity of eachsegment during the commingled production at various choke valvessettings. Each segment's productivity index (PI) can be determined inreal time and can be applied to the model to generate a commingled PI asdefined by:

$\begin{matrix}{{PI} = \frac{Q}{P_{res} - P_{wf}}} & (1)\end{matrix}$where Q is the segment rate, P_(res) is the reservoir pressure, andP_(wf) is the well flowing bottomhole pressure.

After the model is in place, the user can define certain triggers thatare selected to indicate one or more changes in flow conditionsexceeding an absolute or relative threshold, such as a change in theflow rate, water cut, wellhead pressure, or downhole pressure. In someimplementations, the thresholds can be specified in background code(before running the optimization). In some implementations, the triggerscan be defined in a user interface such as the user interface presentedin FIG. 3 . The triggers can be defined by selecting preset thresholdsor through user input fields that allow the user to input and modify thetriggers at any time during the process. In some implementations,thresholds can be set and modified by the user using drop-down menus ina graphical user interface (GUI) with an options list (for example, forflow rate, water cut, wellhead pressure, and downhole pressure) fromwhich the user can enter threshold values.

If the change exceeds a threshold defined by the engineer, the model canbe recalibrated and optimization can be re-initiated. In someimplementations, thresholds can be defined as absolute values orrelative values (for example, percentages of change). In someimplementations, the thresholds can be defined relative to a timeperiod, such as an X % drop in pressure over a time period Y, or anabsolute pressure change P over the time period Z.

In a third main step of the SWC procedure, optimization runs can beperformed on the nodal model to find ICV settings that yield a greatestproduction (for example, oil production), a least water production rate,or combination of both. In some implementations, optimization algorithms(for example, for multi-segmented wells) can use artificial intelligencetechniques such as a genetic algorithm (GA). Genetic algorithms includestochastic and heuristic search techniques based on the theory ofnatural selection and evolution to achieve a “survival of the fittest”solution. The use of such algorithms can lead to suggesting ICV settingsin multi-segmented wells, which can then be used as input for a nodalmodel. In return, the result of the nodal model can be fed back into thealgorithm to evaluate each solution presented by the algorithm.

In some implementations, the optimization can be completed using anoptimization loop, for example, as shown in FIG. 4 and summarized by thefollowing steps. Control parameters are defined, and their feasiblelimits are identified. A diverse pool of possible initial solutions iscreated, where the pool honors parameter limits and covers the solutionspace. For example, engineers can use their previous oil industryexperience in constructing the initial solutions. An objective functionis defined (for example, based on a net present value and a total oilproduction). The objective function is evaluated for each solution,where the evaluation provides a reflection of the solution quality.Current solutions are ranked according to the value of the objectivefunction. A check for convergence is performed. Convergence can be saidto occur when, for example, no further change to the objective functionoccurs for three consecutive iterations. Genetic algorithm reproductionoperators are applied to the current solutions. The steps can berepeated (including defining the objective function through applying thegenetic algorithm) until the best-performing solution is identifiedafter convergence is declared as the optimum solution

In some implementations, the optimization algorithm can be formulated asa programming platform application (for example, matrix laboratory(MATLAB™)) GUI function. When executed, a front-end to the optimizationalgorithm can ask the user to specify a file location of the calibratednodal analysis model file. Information can be extracted from thecalibrated nodal analysis model file. The optimization can be run.Optimal output of the optimization can be provided in tabular formatsimilar to what is shown in FIG. 3 . Each column in the table representsone iteration in the optimization. The final column represents theoptimum solution (for example, in this case the optimization recommendsthat all four ICVs should be fully open). The gain in production aspredicted by the calibrated model is shown in the plot (for this caseabout 7% production gain as compared to the initial solution). Thisoptimized solution reflects the current state of the calibrated model.If this state changes, another optimization can be performed on the newconditions as dictated by the triggers described earlier. Theprogramming platform application can output an executable file that canbe read by the control panel to automatically implement the ICV settingschange.

As a fourth main step of the SWC procedure, the well engineer canreceive the suggested optimal ICV settings and review them. For example,the optimal ICV settings can be displayed to the user in a userinterface. If the user approves the suggested optimal ICV settings, theuser can select an option or a control in the user interface to push thesettings to a control panel for the well, where the suggested optimalICV settings can be implemented in the well. The control panel can beeither located in the well site or inside the gas-oil separation plant(GOSP) for i-field enabled fields. Once the optimal settings arereceived at the control panel from the software, the engineer can inputthe settings in the computer connected to the control panel. In thisway, facilities that do not allow unmanned changes to the well settings(for example, due to security issues). In some implementations,optimization algorithms can be installed in the control panel computer,and changes can be implemented automatically as soon as the optimizationis finished).

In some implementations, if an optimization algorithms is installed in aGOSP, a direct communication can exist between the program and the well.For example, the communication can be wired from the GOSP to a remoteterminal unit (RTU) located near the well. A wired or wirelesscommunication can exist from the RTU to the ICV panel on the well. Thistype of open communication channel can allow for a closed loopoptimization in real-time. In some implementations, testing of theoptimization algorithms can be implemented with the optimization programpushing the results to a reservoir simulation model, which can emulatesthe well performance.

FIG. 3 is a screenshot of an example of an ICV optimization graphicaluser interface (GUI) 300, according to some implementations of thepresent disclosure. In some implementations, the ICV optimization GUI300 can be used with optimization algorithms. The ICV optimization GUI300 can be formulated or implemented as a programming platformapplication GUI and function. When executed, the GUI can ask the user tospecify a location 302 of a calibrated nodal analysis model file to beused by the model. The function can extract the needed information,execute the optimization (for example, when a control 304 is selected),and provide the optimal output in tabular format in a table 306 forpresentation in the GUI. Each column in the table 306 represents asingle iteration 308 in the optimization. A final column 308 a canrepresent an optimum solution. In this example, the optimization canrecommend that all four ICVs 310 (for example, corresponding tosegments) should be fully open, as indicated as an opening fraction(where 1.0=fully open). The gain in production, as predicted by thecalibrated model, is shown in a plot 312 (for example, indicating anapproximate 7% production gain as compared to the initial solution). Insome implementations, a control 314 can be used to import a nodal modelresult. A control 316 can be used to initiate a display of a progressbar indicating a completion progress of the optimization.

In some implementations, the GUI 300 can be designed and created, forexample, using a user interface (UI) design package or a programmingplatform application, such as MATLAB™. For example, a UI design packagecan provide a GUI that allows a user to place figures, tables, andbuttons (for example, to design the GUI 300) and edit text andproperties associated with elements in the UI. Each clickable button inthe UI can be linked to its corresponding code.

When the GUI 300 is initially displayed (for example, before anoptimization is run), the plot 312 and the table 306 are empty. Theuser's first action can be to click on the control 314 (for example, an“Import Nodal Model Results” button). In this example, it is assumedthat a nodal analysis model has already been run and the output hasalready been saved in a format that is compatible with the UI. Forexample, the output that has been saved from the analysis includes theICV settings and their relationships to flow rates from each segment.

After the user clicks on the control 314, the system can display a popupthat the user can use to locate the file containing the ICV settingsversus the segment flow rate data. The popup can include controls forbrowsing a hierarchical file structure, for example, to locate the file.

After the file is selected, the user can use a “Number of ICVs” control303 to enter a number of ICVs in the well (for example, four). The usercan also use a “Number of Iterations” control 305 to enter a number ofiterations for which an optimization is to be run. User selection offewer iterations will produce quicker results. If processing time is nota concern for the user, then the number of iterations can be increased.Optimizations can be stopped if no further improvement on the solutionis observed, for example, between successive runs.

After the number of ICVs and iterations have been entered, the user canclick on the “Run Optimization” button (control 304). This triggers thebeginning of the optimization. The optimization progression (control316) becomes active. At this time, the table 306 can be updated (androws labeled) to reflect the number of ICVs (for example, four) selectedby the user.

The plot 312, table 306, and optimization progress bar can be updatedafter each iteration, including adding another column to the table 306for each iteration. The first iteration can represent the best solutionfrom the input file, which may be only a subset of all possiblesolutions, as the nodal analysis model only considers a few possible ICVsettings for each segment. Subsequent iterations can apply the geneticalgorithm to improve on the optimal solution from each previousiteration.

The plot 312 shown in FIG. 3 indicates the results after theoptimization has completed. In this example, the optimal solutionprovides approximately a 7% production increase over the base case after14 iterations. Also, the optimal solution reached by the algorithmindicates that all four ICVs are to be kept fully open (as defined by asetting of 1, indicating 100% open).

FIG. 4 is a flow diagram showing an example of an overall optimizationworkflow 400, according to some implementations of the presentdisclosure. The workflow 400 includes a continuous automatedoptimization procedure. The procedure can start with data collection andend by applying the model solution to the field. The workflow includessteps techniques for SWC optimization to i-fields and data collectiondescribed in U.S. patent application Ser. No. 16/414,535, filed on May16, 2019, titled “Automated Production Optimization Technique for SmartWell Completions Using Real-Time Nodal Analysis,” as previouslyintroduced.

At 402, a nodal model is constructed, for example, based on techniquesU.S. patent application Ser. No. 16/414,535. At 404, real-time data isscanned, for example, using information received or retrieved from anoil production facility. For example, a developed programming platformapplication algorithm can include a module for data parsing into avector (for example, representing a parameter as a function of time) ora matrix (for example, if several parameters exist). In someimplementations, when the algorithm is run for a specific time interval(for example, with a data location specified), the parameters associatedwith the time interval can be imported into the programming platformapplication space to initiate the optimization. Production fields anddevelopments can include an i-Field platform, where the well data(including, for example, pressure, temperature, and flow rate) can bedisplayed in real time through the PI system. In some implementations,Excel, programming platform application, or a structured query language(SQL) server can be used to query the PI database and download a sectionof the data for a specific well or a group of wells for a defined timeperiod. Once the data is downloaded, the data can be imported into theoptimization software.

At 406, a determination is made whether a change in triggers exceeds athreshold defined, for example, by the engineer or by an automatedsystem or process. If not, then the previous ICV settings are kept at408, and the workflow 400 returns to step 404. When the determinationindicated that the change in the triggers exceeds the threshold, thenthe model is recalibrated and used to calculate new PI values at 410. At412, the optimization algorithm is run on the calibrated model todetermine new optimal ICV control settings.

At 414, a determination is made whether the new settings improveproduction. In some implementations, the determination can be made usinga simple comparison with the flow rate from the previous ICV settings.For example, if the new oil rate is greater than the old oil rate, thena production improvement is indicated. In other words, the productionimprovement can be the difference between the production with the newsettings minus the production with the old settings (with a positivevalue indicating production improvement). In some implementations, othercriteria can be specified by the user, such as criteria associated withreduced water production, production profile equalization (for example,equal production from the different ICVs), or maintaining a drawdown(bottomhole pressure minus reservoir pressure) within a user-specifiedrange. These types of criteria can be implemented within applicationcode (for example, within a backend), or implemented using GUI drop-downmenus, or as a combination.

If not, then the workflow returns to step 408, keeping the previous ICVsettings. If the new settings are determined to improved production,then the new settings are applied to the field at 416. Then the workflow400 can resume at step 404, where real-time data is scanned, this timeusing the applied new settings.

In some implementations, various well production conditions can existthat improve the results of optimization. For example, better resultscan be achieved when healthy surface and subsurface equipment exists,such as ICVs, permanent downhole monitors (PDHMs), and multi-phase flowmeter (MPFM) that give accurate measurement of well rate and downholepressure.

Smart well components, such as rate, downhole pressure gauges and ICVs,need to be regularly maintained, inspected and operative to carry-outthe described invention and gain desired benefits of the inventedapproach.

FIG. 5 is a set diagram showing an example of an intersection 500 ofdifferent well types, according to some implementations of the presentdisclosure. The different well types include ICV wells 502 and gas lift(GL) wells 504. Wells 506 include aspects of ICV wells 502 and GL wells504.

Systems for ICV wells 502 and GL wells 504 are independent, but thewells can co-exist. For example, well productivity can be enhanced usingdifferent aspects, for example, GL to help lift downhole fluid whenthere is not enough pressure support, and ICVs to provide more reservoircontrol to maximize oil and minimize water production.

The techniques described in the present disclosure can be used in ICVwells, but not in GL wells, where only some types of methods, forexample, can be used. In common areas (for example, wells 506 thatinclude aspects of ICV wells 502 and GL wells 504), the techniquesdescribed in the present disclosure can be used. For example, theworkflow 400 can be used to optimize production from ICVs, and Queralesmethod can be used to optimize GL system performance, or both techniquescan be used simultaneously to improve performance of their respectivesystem.

FIG. 6 is a flowchart of an example method 600 for determining newoptimal ICV settings for improving production at a multi-segmented oilproduction facility and providing the optimal ICV settings to a controlpanel for the multi-segmented oil production facility, according to someimplementations of the present disclosure. For clarity of presentation,the description that follows generally describes method 600 in thecontext of the other figures in this description. However, it will beunderstood that method 600 can be performed, for example, by anysuitable system, environment, software, and hardware, or a combinationof systems, environments, software, and hardware, as appropriate. Insome implementations, various steps of method 600 can be run inparallel, in combination, in loops, or in any order.

At 602, a multi-segmented well production model representing productionat a multi-segmented oil production facility is calibrated. Themulti-segmented well production model models production based on wellrates and flowing bottom-hole pressure data at various choke settingsfor multiple flow conditions for each segment of the multi-segmentedwell. As an example, the model described with reference to FIG. 1 can becalibrated. The multiple flow conditions for which the model is set upcan include commingled testing and individual segment testing, forexample. From 602, method 600 proceeds to 604.

At 604, real-time updates to the well rates and the flowing bottom-holepressure data are received from the multi-segmented oil productionfacility. For example, real-time information can be received by the PIsystem 204 from the well 202. From 604, method 600 proceeds to 606.

At 606, changes to triggers identifying thresholds for identifyingproduction improvements are received based on user inputs. As anexample, the user can use the GUI 300 or UIs accessible through the GUI300 to input numerical thresholds that are to be used for settingsthresholds. From 606, method 600 proceeds to 608.

At 608, the multi-segmented well production model is re-calibrated basedon the changes to the triggers and the real-time updates to the wellrates and the flowing bottom-hole pressure data. For example, real-timeinformation from the well 200 can be used to calibrate the model. From608, method 600 proceeds to 610.

At 610, using the re-calibrated nodal model, an optimization algorithmis executed to determine new optimal inflow control valve (ICV)settings. For example, the optimization algorithm can identify settingsto maximize multi-segmented well productivity, restrict unwanted waterand gas production in the multi-segmented well, and improve sweepefficiency for the multi-segmented well. From 610, method 600 proceedsto 612.

At 612, using the re-calibrated multi-segmented well production model, adetermination is made whether the new optimal ICV settings improveproduction at the multi-segmented oil production facility. As anexample, the system can determine whether the new optimal ICV settingsimprove one or more of multi-segmented well productivity, restrictedunwanted water and gas production, and sweep efficiency. Thedetermination can include determining if the improvement is greater thana pre-determined threshold, for example, an absolute value or a relativevalue (for example, a predetermined percentage). From 612, method 600proceeds to 614.

At 614, in response to determining that the new optimal ICV settingsimprove production, the optimal ICV settings are provided to a controlpanel for the multi-segmented oil production facility. For example,using a UI such as the GUI 300, the user can make a selection on ascreen to send the optimal ICV settings to a control panel, where theoptimal ICV settings can be selected for real-time implementation.

In some implementations, providing the optimal ICV settings to thecontrol panel for the multi-segmented oil production facility includessending, by a multi-segmented well optimizing system, control commandsto the control panel. The control commands can include, for example,commands to change settings for surface ICVs and subsurface ICVs in oneor more segments of the multi-segmented well, and choke settingscommands to set different choke settings on different ICVs. After 614,method 600 can stop.

In some implementations, providing the optimal ICV settings to thecontrol panel includes providing an executable file output by aprogramming platform application program. In this example, method 600can further include executing, by the control panel, the executable fileto automatically implement the optimal ICV settings.

FIG. 7 is a block diagram of an example computer system 700 used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and proceduresdescribed in the present disclosure, according to some implementationsof the present disclosure. The illustrated computer 702 is intended toencompass any computing device such as a server, a desktop computer, alaptop/notebook computer, a wireless data port, a smart phone, apersonal data assistant (PDA), a tablet computing device, or one or moreprocessors within these devices, including physical instances, virtualinstances, or both. The computer 702 can include input devices such askeypads, keyboards, and touch screens that can accept user information.Also, the computer 702 can include output devices that can conveyinformation associated with the operation of the computer 702. Theinformation can include digital data, visual data, audio information, ora combination of information. The information can be presented in agraphical user interface (UI) (or GUI).

The computer 702 can serve in a role as a client, a network component, aserver, a database, a persistency, or components of a computer systemfor performing the subject matter described in the present disclosure.The illustrated computer 702 is communicably coupled with a network 730.In some implementations, one or more components of the computer 702 canbe configured to operate within different environments, includingcloud-computing-based environments, local environments, globalenvironments, and combinations of environments.

At a top level, the computer 702 is an electronic computing deviceoperable to receive, transmit, process, store, and manage data andinformation associated with the described subject matter. According tosome implementations, the computer 702 can also include, or becommunicably coupled with, an application server, an email server, a webserver, a caching server, a streaming data server, or a combination ofservers.

The computer 702 can receive requests over network 730 from a clientapplication (for example, executing on another computer 702). Thecomputer 702 can respond to the received requests by processing thereceived requests using software applications. Requests can also be sentto the computer 702 from internal users (for example, from a commandconsole), external (or third) parties, automated applications, entities,individuals, systems, and computers.

Each of the components of the computer 702 can communicate using asystem bus 703. In some implementations, any or all of the components ofthe computer 702, including hardware or software components, caninterface with each other or the interface 704 (or a combination ofboth) over the system bus 703. Interfaces can use an applicationprogramming interface (API) 712, a service layer 713, or a combinationof the API 712 and service layer 713. The API 712 can includespecifications for routines, data structures, and object classes. TheAPI 712 can be either computer-language independent or dependent. TheAPI 712 can refer to a complete interface, a single function, or a setof APIs.

The service layer 713 can provide software services to the computer 702and other components (whether illustrated or not) that are communicablycoupled to the computer 702. The functionality of the computer 702 canbe accessible for all service consumers using this service layer.Software services, such as those provided by the service layer 713, canprovide reusable, defined functionalities through a defined interface.For example, the interface can be software written in JAVA, C++, or alanguage providing data in extensible markup language (XML) format.While illustrated as an integrated component of the computer 702, inalternative implementations, the API 712 or the service layer 713 can bestand-alone components in relation to other components of the computer702 and other components communicably coupled to the computer 702.Moreover, any or all parts of the API 712 or the service layer 713 canbe implemented as child or sub-modules of another software module,enterprise application, or hardware module without departing from thescope of the present disclosure.

The computer 702 includes an interface 704. Although illustrated as asingle interface 704 in FIG. 7 , two or more interfaces 704 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 702 and the described functionality. The interface 704 canbe used by the computer 702 for communicating with other systems thatare connected to the network 730 (whether illustrated or not) in adistributed environment. Generally, the interface 704 can include, or beimplemented using, logic encoded in software or hardware (or acombination of software and hardware) operable to communicate with thenetwork 730. More specifically, the interface 704 can include softwaresupporting one or more communication protocols associated withcommunications. As such, the network 730 or the interface's hardware canbe operable to communicate physical signals within and outside of theillustrated computer 702.

The computer 702 includes a processor 705. Although illustrated as asingle processor 705 in FIG. 7 , two or more processors 705 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 702 and the described functionality. Generally, theprocessor 705 can execute instructions and can manipulate data toperform the operations of the computer 702, including operations usingalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure.

The computer 702 also includes a database 706 that can hold data for thecomputer 702 and other components connected to the network 730 (whetherillustrated or not). For example, database 706 can be an in-memory,conventional, or a database storing data consistent with the presentdisclosure. In some implementations, database 706 can be a combinationof two or more different database types (for example, hybrid in-memoryand conventional databases) according to particular needs, desires, orparticular implementations of the computer 702 and the describedfunctionality. Although illustrated as a single database 706 in FIG. 7 ,two or more databases (of the same, different, or combination of types)can be used according to particular needs, desires, or particularimplementations of the computer 702 and the described functionality.While database 706 is illustrated as an internal component of thecomputer 702, in alternative implementations, database 706 can beexternal to the computer 702.

The computer 702 also includes a memory 707 that can hold data for thecomputer 702 or a combination of components connected to the network 730(whether illustrated or not). Memory 707 can store any data consistentwith the present disclosure. In some implementations, memory 707 can bea combination of two or more different types of memory (for example, acombination of semiconductor and magnetic storage) according toparticular needs, desires, or particular implementations of the computer702 and the described functionality. Although illustrated as a singlememory 707 in FIG. 7 , two or more memories 707 (of the same, different,or combination of types) can be used according to particular needs,desires, or particular implementations of the computer 702 and thedescribed functionality. While memory 707 is illustrated as an internalcomponent of the computer 702, in alternative implementations, memory707 can be external to the computer 702.

The application 708 can be an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 702 and the described functionality. Forexample, application 708 can serve as one or more components, modules,or applications. Further, although illustrated as a single application708, the application 708 can be implemented as multiple applications 708on the computer 702. In addition, although illustrated as internal tothe computer 702, in alternative implementations, the application 708can be external to the computer 702.

The computer 702 can also include a power supply 714. The power supply714 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 714 can include power-conversion andmanagement circuits, including recharging, standby, and power managementfunctionalities. In some implementations, the power-supply 714 caninclude a power plug to allow the computer 702 to be plugged into a wallsocket or a power source to, for example, power the computer 702 orrecharge a rechargeable battery.

There can be any number of computers 702 associated with, or externalto, a computer system containing computer 702, with each computer 702communicating over network 730. Further, the terms “client,” “user,” andother appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the present disclosure.Moreover, the present disclosure contemplates that many users can useone computer 702 and one user can use multiple computers 702.

Described implementations of the subject matter can include one or morefeatures, alone or in combination.

For example, in a first implementation, a computer-implemented method,including the following. A multi-segmented well production modelrepresenting production at a multi-segmented oil production facility iscalibrated. The multi-segmented well production model models productionbased on well rates and flowing bottom-hole pressure data at variouschoke settings for multiple flow conditions for each segment of themulti-segmented well. Real-time updates to the well rates and theflowing bottom-hole pressure data are received from the multi-segmentedoil production facility. Changes to triggers identifying thresholds foridentifying production improvements are received based on user inputs.The multi-segmented well production model is re-calibrated based on thechanges to the triggers and the real-time updates to the well rates andthe flowing bottom-hole pressure data. Using the re-calibrated nodalmodel, an optimization algorithm is executed to determine new optimalinflow control valve (ICV) settings. Using the re-calibratedmulti-segmented well production model, a determination is made whetherthe new optimal ICV settings improve production at the multi-segmentedoil production facility. In response to determining that the new optimalICV settings improve production, the optimal ICV settings are providedto a control panel for the multi-segmented oil production facility.

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, wherethe multiple flow conditions include commingled testing and individualsegment testing.

A second feature, combinable with any of the previous or followingfeatures, where the optimization algorithm maximizes multi-segmentedwell productivity, restricts unwanted water and gas production in themulti-segmented well, and improves sweep efficiency for themulti-segmented well.

A third feature, combinable with any of the previous or followingfeatures, where providing the optimal ICV settings to the control panelfor the multi-segmented oil production facility includes sending, by amulti-segmented well optimizing system, control commands to the controlpanel.

A fourth feature, combinable with any of the previous or followingfeatures, where the control commands include commands to change settingsfor surface ICVs and subsurface ICVs in one or more segments of themulti-segmented well.

A fifth feature, combinable with any of the previous or followingfeatures, where the control commands include choke settings commands toset different choke settings on different ICVs.

A sixth feature, combinable with any of the previous or followingfeatures, where providing the optimal ICV settings to the control panelincludes providing an executable file output by a programming platformapplication, and where the computer-implemented method further includesexecuting, by the control panel, the executable file to automaticallyimplement the optimal ICV settings.

In a second implementation, a non-transitory, computer-readable mediumstoring one or more instructions executable by a computer system toperform operations including the following. A multi-segmented wellproduction model representing production at a multi-segmented oilproduction facility is calibrated. The multi-segmented well productionmodel models production based on well rates and flowing bottom-holepressure data at various choke settings for multiple flow conditions foreach segment of the multi-segmented well. Real-time updates to the wellrates and the flowing bottom-hole pressure data are received from themulti-segmented oil production facility. Changes to triggers identifyingthresholds for identifying production improvements are received based onuser inputs. The multi-segmented well production model is re-calibratedbased on the changes to the triggers and the real-time updates to thewell rates and the flowing bottom-hole pressure data. Using there-calibrated nodal model, an optimization algorithm is executed todetermine new optimal inflow control valve (ICV) settings. Using there-calibrated multi-segmented well production model, a determination ismade whether the new optimal ICV settings improve production at themulti-segmented oil production facility. In response to determining thatthe new optimal ICV settings improve production, the optimal ICVsettings are provided to a control panel for the multi-segmented oilproduction facility.

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, wherethe multiple flow conditions include commingled testing and individualsegment testing.

A second feature, combinable with any of the previous or followingfeatures, where the optimization algorithm maximizes multi-segmentedwell productivity, restricts unwanted water and gas production in themulti-segmented well, and improves sweep efficiency for themulti-segmented well.

A third feature, combinable with any of the previous or followingfeatures, where providing the optimal ICV settings to the control panelfor the multi-segmented oil production facility includes sending, by amulti-segmented well optimizing system, control commands to the controlpanel.

A fourth feature, combinable with any of the previous or followingfeatures, where the control commands include commands to change settingsfor surface ICVs and subsurface ICVs in one or more segments of themulti-segmented well.

A fifth feature, combinable with any of the previous or followingfeatures, where the control commands include choke settings commands toset different choke settings on different ICVs.

A sixth feature, combinable with any of the previous or followingfeatures, where providing the optimal ICV settings to the control panelincludes providing an executable file output by a programming platformapplication, and where the operations further include executing, by thecontrol panel, the executable file to automatically implement theoptimal ICV settings.

In a third implementation, a computer-implemented system, including oneor more processors and a non-transitory computer-readable storage mediumcoupled to the one or more processors and storing programminginstructions for execution by the one or more processors, theprogramming instructions instructing the one or more processors toperform operations including the following. A multi-segmented wellproduction model representing production at a multi-segmented oilproduction facility is calibrated. The multi-segmented well productionmodel models production based on well rates and flowing bottom-holepressure data at various choke settings for multiple flow conditions foreach segment of the multi-segmented well. Real-time updates to the wellrates and the flowing bottom-hole pressure data are received from themulti-segmented oil production facility. Changes to triggers identifyingthresholds for identifying production improvements are received based onuser inputs. The multi-segmented well production model is re-calibratedbased on the changes to the triggers and the real-time updates to thewell rates and the flowing bottom-hole pressure data. Using there-calibrated nodal model, an optimization algorithm is executed todetermine new optimal inflow control valve (ICV) settings. Using there-calibrated multi-segmented well production model, a determination ismade whether the new optimal ICV settings improve production at themulti-segmented oil production facility. In response to determining thatthe new optimal ICV settings improve production, the optimal ICVsettings are provided to a control panel for the multi-segmented oilproduction facility.

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, wherethe multiple flow conditions include commingled testing and individualsegment testing.

A second feature, combinable with any of the previous or followingfeatures, where the optimization algorithm maximizes multi-segmentedwell productivity, restricts unwanted water and gas production in themulti-segmented well, and improves sweep efficiency for themulti-segmented well.

A third feature, combinable with any of the previous or followingfeatures, where providing the optimal ICV settings to the control panelfor the multi-segmented oil production facility includes sending, by amulti-segmented well optimizing system, control commands to the controlpanel.

A fourth feature, combinable with any of the previous or followingfeatures, where the control commands include commands to change settingsfor surface ICVs and subsurface ICVs in one or more segments of themulti-segmented well.

A fifth feature, combinable with any of the previous or followingfeatures, where the control commands include choke settings commands toset different choke settings on different ICVs.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Software implementations of the described subjectmatter can be implemented as one or more computer programs. Eachcomputer program can include one or more modules of computer programinstructions encoded on a tangible, non-transitory, computer-readablecomputer-storage medium for execution by, or to control the operationof, data processing apparatus. Alternatively, or additionally, theprogram instructions can be encoded in/on an artificially generatedpropagated signal. For example, the signal can be a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to a suitable receiver apparatus forexecution by a data processing apparatus. The computer-storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofcomputer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware. For example, a dataprocessing apparatus can encompass all kinds of apparatuses, devices,and machines for processing data, including by way of example, aprogrammable processor, a computer, or multiple processors or computers.The apparatus can also include special purpose logic circuitryincluding, for example, a central processing unit (CPU), afield-programmable gate array (FPGA), or an application-specificintegrated circuit (ASIC). In some implementations, the data processingapparatus or special purpose logic circuitry (or a combination of thedata processing apparatus or special purpose logic circuitry) can behardware- or software-based (or a combination of both hardware- andsoftware-based). The apparatus can optionally include code that createsan execution environment for computer programs, for example, code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, or a combination of execution environments.The present disclosure contemplates the use of data processingapparatuses with or without conventional operating systems, such asLINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language.Programming languages can include, for example, compiled languages,interpreted languages, declarative languages, or procedural languages.Programs can be deployed in any form, including as stand-alone programs,modules, components, subroutines, or units for use in a computingenvironment. A computer program can, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data, for example, one or more scripts stored ina markup language document, in a single file dedicated to the program inquestion, or in multiple coordinated files storing one or more modules,sub-programs, or portions of code. A computer program can be deployedfor execution on one computer or on multiple computers that are located,for example, at one site or distributed across multiple sites that areinterconnected by a communication network. While portions of theprograms illustrated in the various figures may be shown as individualmodules that implement the various features and functionality throughvarious objects, methods, or processes, the programs can instead includea number of sub-modules, third-party services, components, andlibraries. Conversely, the features and functionality of variouscomponents can be combined into single components as appropriate.Thresholds used to make computational determinations can be statically,dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specificationcan be performed by one or more programmable computers executing one ormore computer programs to perform functions by operating on input dataand generating output. The methods, processes, or logic flows can alsobe performed by, and apparatus can also be implemented as, specialpurpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon one or more of general and special purpose microprocessors and otherkinds of CPUs. The elements of a computer are a CPU for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a CPU can receive instructions anddata from (and write data to) a memory. A computer can also include, orbe operatively coupled to, one or more mass storage devices for storingdata. In some implementations, a computer can receive data from, andtransfer data to, the mass storage devices including, for example,magnetic, magneto-optical disks, or optical disks. Moreover, a computercan be embedded in another device, for example, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a global positioning system (GPS) receiver, or a portablestorage device such as a universal serial bus (USB) flash drive.

Computer-readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data can includeall forms of permanent/non-permanent and volatile/non-volatile memory,media, and memory devices. Computer-readable media can include, forexample, semiconductor memory devices such as random access memory(RAM), read-only memory (ROM), phase change memory (PRAM), static randomaccess memory (SRAM), dynamic random access memory (DRAM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices.Computer-readable media can also include, for example, magnetic devicessuch as tape, cartridges, cassettes, and internal/removable disks.Computer-readable media can also include magneto-optical disks andoptical memory devices and technologies including, for example, digitalvideo disc (DVD), CD-ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, andBLU-RAY. The memory can store various objects or data, including caches,classes, frameworks, applications, modules, backup data, jobs, webpages, web page templates, data structures, database tables,repositories, and dynamic information. Types of objects and data storedin memory can include parameters, variables, algorithms, instructions,rules, constraints, and references. Additionally, the memory can includelogs, policies, security or access data, and reporting files. Theprocessor and the memory can be supplemented by, or incorporated into,special purpose logic circuitry.

Implementations of the subject matter described in the presentdisclosure can be implemented on a computer having a display device forproviding interaction with a user, including displaying information to(and receiving input from) the user. Types of display devices caninclude, for example, a cathode ray tube (CRT), a liquid crystal display(LCD), a light-emitting diode (LED), and a plasma monitor. Displaydevices can include a keyboard and pointing devices including, forexample, a mouse, a trackball, or a trackpad. User input can also beprovided to the computer through the use of a touchscreen, such as atablet computer surface with pressure sensitivity or a multi-touchscreen using capacitive or electric sensing. Other kinds of devices canbe used to provide for interaction with a user, including to receiveuser feedback including, for example, sensory feedback including visualfeedback, auditory feedback, or tactile feedback. Input from the usercan be received in the form of acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents to,and receiving documents from, a device that the user uses. For example,the computer can send web pages to a web browser on a device for a useror client in response to requests received from the web browser.

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, including,but not limited to, a web browser, a touch-screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements can be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server. Moreover, the computingsystem can include a front-end component, for example, a client computerhaving one or both of a graphical user interface or a Web browserthrough which a user can interact with the computer. The components ofthe system can be interconnected by any form or medium of wireline orwireless digital data communication (or a combination of datacommunication) in a communication network. Examples of communicationnetworks include a local area network (LAN), a radio access network(RAN), a metropolitan area network (MAN), a wide area network (WAN),Worldwide Interoperability for Microwave Access (WIMAX), a wirelesslocal area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20or a combination of protocols), all or a portion of the Internet, or anyother communication system or systems at one or more locations (or acombination of communication networks). The network can communicatewith, for example, Internet Protocol (IP) packets, frame relay frames,asynchronous transfer mode (ATM) cells, voice, video, data, or acombination of communication types between network addresses.

The computing system can include clients and servers. A client andserver can generally be remote from each other and can typicallyinteract through a communication network. The relationship of client andserver can arise by virtue of computer programs running on therespective computers and having a client-server relationship.

Cluster file systems can be any file system type accessible frommultiple servers for read and update. Locking or consistency trackingmay not be necessary since the locking of exchange file system can bedone at application layer. Furthermore, Unicode data files can bedifferent from non-Unicode data files.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations can also beimplemented, in combination, in a single implementation. Conversely,various features that are described in the context of a singleimplementation can also be implemented in multiple implementations,separately, or in any suitable sub-combination. Moreover, althoughpreviously described features may be described as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations may be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations. It should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously described example implementations do notdefine or constrain the present disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer system includinga computer memory interoperably coupled with a hardware processorconfigured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

What is claimed is:
 1. A computer-implemented method, comprising: calibrating, by a well production control system, a multi-segmented well production model representing production at a multi-segmented oil production facility, wherein the multi-segmented well production model models production based on well rates and flowing bottom-hole pressure data at various choke settings for multiple flow conditions for each segment of a multi-segmented well; receiving, by the well production control system from the multi-segmented oil production facility, real-time updates to the well rates and the flowing bottom-hole pressure data; receiving, by the well production control system, user inputs defining triggers for identifying thresholds for determining that production improvements have occurred, wherein the thresholds include one or both of a relative threshold defining a relative change in pressure over a first time period and an absolute threshold defining an absolute change in pressure over a second time period; determining, by the well production control system using the defined triggers, that changes in production improvements exceeding the thresholds have occurred; determining, by the well production control system, that a production interval has occurred; re-calibrating, by the well production control system using the real-time updates to the well rates and the flowing bottom-hole pressure data, the multi-segmented well production model; iterating, using a number of iterations based on learned patterns of correlating the number of iterations with a number of variables, executions of an optimization algorithm of the multi-segmented well production model, including: executing, by the well production control system using the re-calibrated multi-segmented well production model, the optimization algorithm to determine new optimal inflow control valve (ICV) settings; comparing, by the well production control system, a flow rate determined from the new optimal ICV settings with a flow rate using previous ICV settings; and determining, by the well production control system based on the comparing and the re-calibrated multi-segmented well production model, that the new optimal ICV settings result in an improvement of production at the multi-segmented oil production facility, the improvement being at least a pre-determined percentage above a production rate using previous ICV settings; and providing, by the well production control system, the optimal ICV settings to a control panel for the multi-segmented oil production facility.
 2. The computer-implemented method of claim 1, wherein the multiple flow conditions include commingled testing and individual segment testing.
 3. The computer-implemented method of claim 1, wherein the optimization algorithm maximizes multi-segmented well productivity, restricts unwanted water and gas production in the multi-segmented well, and improves sweep efficiency for the multi-segmented well.
 4. The computer-implemented method of claim 1, wherein providing the optimal ICV settings to the control panel for the multi-segmented oil production facility includes sending, by a multi-segmented well optimizing system, control commands to the control panel.
 5. The computer-implemented method of claim 4, wherein the control commands include commands to change settings for surface ICVs and subsurface ICVs in one or more segments of the multi-segmented well.
 6. The computer-implemented method of claim 5, wherein the control commands include choke settings commands to set different choke settings on different ICVs.
 7. The computer-implemented method of claim 1, wherein providing the optimal ICV settings to the control panel includes providing an executable file output by a programming platform application, and wherein the computer-implemented method further comprises executing, by the control panel, the executable file to automatically implement the optimal ICV settings.
 8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: calibrating, by a well production control system, a multi-segmented well production model representing production at a multi-segmented oil production facility, wherein the multi-segmented well production model models production based on well rates and flowing bottom-hole pressure data at various choke settings for multiple flow conditions for each segment of a multi-segmented well; receiving, by the well production control system from the multi-segmented oil production facility, real-time updates to the well rates and the flowing bottom-hole pressure data; receiving, by the well production control system, user inputs defining triggers for identifying thresholds for determining that production improvements have occurred, wherein the thresholds include one or both of a relative threshold defining a relative change in pressure over a first time period and an absolute threshold defining an absolute change in pressure over a second time period; determining, by the well production control system using the defined triggers, that changes in production improvements exceeding the thresholds have occurred; determining, by the well production control system, that a production interval has occurred; re-calibrating, by the well production control system using the real-time updates to the well rates and the flowing bottom-hole pressure data, the multi-segmented well production model; iterating, using a number of iterations based on learned patterns of correlating the number of iterations with a number of variables, executions of an optimization algorithm of the multi-segmented well production model, including: executing, by the well production control system using the re-calibrated multi-segmented well production model, the optimization algorithm to determine new optimal inflow control valve (ICV) settings; comparing, by the well production control system, a flow rate determined from the new optimal ICV settings with a flow rate using previous ICV settings; and determining, by the well production control system based on the comparing and the re-calibrated multi-segmented well production model, that the new optimal ICV settings result in an improvement of production at the multi-segmented oil production facility, the improvement being at least a pre-determined percentage above a production rate using previous ICV settings; and providing, by the well production control system, the optimal ICV settings to a control panel for the multi-segmented oil production facility.
 9. The non-transitory, computer-readable medium of claim 8, wherein the multiple flow conditions include commingled testing and individual segment testing.
 10. The non-transitory, computer-readable medium of claim 8, wherein the optimization algorithm maximizes multi-segmented well productivity, restricts unwanted water and gas production in the multi-segmented well, and improves sweep efficiency for the multi-segmented well.
 11. The non-transitory, computer-readable medium of claim 8, wherein providing the optimal ICV settings to the control panel for the multi-segmented oil production facility includes sending, by a multi-segmented well optimizing system, control commands to the control panel.
 12. The non-transitory, computer-readable medium of claim 11, wherein the control commands include commands to change settings for surface ICVs and subsurface ICVs in one or more segments of the multi-segmented well.
 13. The non-transitory, computer-readable medium of claim 12, wherein the control commands include choke settings commands to set different choke settings on different ICVs.
 14. The non-transitory, computer-readable medium of claim 8, wherein providing the optimal ICV settings to the control panel includes providing an executable file output by a programming platform application, and wherein the operations further comprise executing, by the control panel, the executable file to automatically implement the optimal ICV settings.
 15. A computer-implemented system, comprising: one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising: calibrating, by a well production control system, a multi-segmented well production model representing production at a multi-segmented oil production facility, wherein the multi-segmented well production model models production based on well rates and flowing bottom-hole pressure data at various choke settings for multiple flow conditions for each segment of a multi-segmented well; receiving, by the well production control system from the multi-segmented oil production facility, real-time updates to the well rates and the flowing bottom-hole pressure data; receiving, by the well production control system, user inputs to defining triggers for identifying thresholds for determining that production improvements have occurred, wherein the thresholds include one or both of a relative threshold defining a relative change in pressure over a first time period and an absolute threshold defining an absolute change in pressure over a second time period; determining, by the well production control system using the defined triggers, that changes in production improvements exceeding the thresholds have occurred; determining, by the well production control system, that a production interval has occurred; re-calibrating, by the well production control system using the real-time updates to the well rates and the flowing bottom-hole pressure data, the multi-segmented well production model; iterating, using a number of iterations based on learned patterns of correlating the number of iterations with a number of variables, executions of an optimization algorithm of the multi-segmented well production model, including: executing, by the well production control system using the re-calibrated multi-segmented well production model, the optimization algorithm to determine new optimal inflow control valve (ICV) settings; comparing, by the well production control system, a flow rate determined from the new optimal ICV settings with a flow rate using previous ICV settings; and determining, by the well production control system based on the comparing and the re-calibrated multi-segmented well production model, that the new optimal ICV settings result in an improvement of production at the multi-segmented oil production facility, the improvement being at least a pre-determined percentage above a production rate using previous ICV settings; and providing, by the well production control system, the optimal ICV settings to a control panel for the multi-segmented oil production facility.
 16. The computer-implemented system of claim 15, wherein the multiple flow conditions include commingled testing and individual segment testing.
 17. The computer-implemented system of claim 15, wherein the optimization algorithm maximizes multi-segmented well productivity, restricts unwanted water and gas production in the multi-segmented well, and improves sweep efficiency for the multi-segmented well.
 18. The computer-implemented system of claim 15, wherein providing the optimal ICV settings to the control panel for the multi-segmented oil production facility includes sending, by a multi-segmented well optimizing system, control commands to the control panel.
 19. The computer-implemented system of claim 18, wherein the control commands include commands to change settings for surface ICVs and subsurface ICVs in one or more segments of the multi-segmented well.
 20. The computer-implemented system of claim 19, wherein the control commands include choke settings commands to set different choke settings on different ICVs. 